I should have been clearer. I'm not talking about making a separate call to the model to ask it to check itself. Any given model essentially is already watching for contradictions all the time as it is generating its output tokens. Frontier models like Claude Opus 4.6 are already exceptionally good at not contradicting themselves as they go. As for not having an external fact base - you could in principle insert content ephemerally into the context that is relevant to the task at hand, though doing this without killing modern prompt caching schemes is challenging.
I saw your benchmarks, what I was asking for is benchmarks of the full system (LLM + the NLI model) vs a frontier LLM on its own. Its fine if you didn't do them, but I think it hurts your case.
I saw your benchmarks, what I was asking for is benchmarks of the full system (LLM + the NLI model) vs a frontier LLM on its own. Its fine if you didn't do them, but I think it hurts your case.